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A 378-page book teaching practical approaches to building ML models by prioritizing data quality over algorithm complexity, covering data collection, cleaning, labeling, and synthetic data generation with Python. Published February 2024, it emphasizes responsible AI and the role of subject-matter experts in model development.
- Shifts focus from algorithm optimization to data quality as the foundation for robust, fair, and interpretable ML models
- Covers concrete techniques: data imputation, cleaning, labeling, augmentation, and synthetic data generation with scikit-learn code examples
- Introduces "small data" concept and strategies for handling missing data, addressing bias, and building ethical AI systems
This article introduces Pointblank, a Python library designed to streamline data validation. It emphasizes user-friendly features, automated validation suggestions, and customizable reports to enhance team communication about data quality issues.
- Pointblank's DraftValidation feature uses AI to scan a dataset and auto-generate suggested validation rules, cutting setup time
- Reports are built for team communication, not just error logs, translating validation results into actionable insights
- It works across Polars, Pandas, and SQL databases, with YAML config support for CI/CD pipelines and shared validation rules
- Supports automated alerting (e.g. Slack notifications) when data quality thresholds for warnings/errors are breached